Tumor Motion Research Articles

3045: First results of real-time motion imaging using integrated-mode ion radiography for tumour motion

3045: First results of real-time motion imaging using integrated-mode ion radiography for tumour motion

Continuous and bilevel positive airway pressure may improve radiotherapy delivery in patients with intra-thoracic tumors

BackgroundMinimizing tumor motion in radiotherapy for intra-thoracic tumors reduces side-effects by limiting radiation exposure to healthy tissue. Continuous or Bilevel Positive Airway Pressure (CPAP/BiPAP) could achieve this, since it could increase lung inflation and decrease tidal volume variability. We aim to identify the better CPAP/BiPAP setting for minimizing tumor motion. MethodsIn 10 patients (5 with lung cancer, 5 with other intra-thoracic tumors), CPAP/BiPAP was tested with the following settings for 10 min each: CPAP 5, 10 and 15 cmH2O and BiPAP 14/10 cmH2O with a lower (7 breaths/min) and higher back-up respiratory rate (BURR initially 1 breath/min above the spontaneous breathing frequency, with the option to adjust if the patient continued to initiate breaths). Electrical impedance tomography was used to analyse end-expiratory lung impedance (EELI) as an estimate of end-expiratory lung volume and tidal impedance variation (TIV) as an estimate of tidal volume. ResultsNine out of ten patients tolerated all settings; one patient could not sustain CPAP-15. A significant difference in EELI was observed between settings (χ2 22.960, p < 0.001), with most increase during CPAP-15 (median (IQR) 1.03 (1.00 – 1.06), normalized to the EELI during spontaneous breathing). No significant differences in TIV and breathing variability were found between settings. ConclusionsThis study shows that the application of different settings of CPAP/BiPAP in patients with intra-thoracic tumors is feasible and tolerable. BiPAP with a higher BURR may offer the greatest potential for mitigating tumor motion among the applied settings, although further research investigating tumor motion should be conducted.

A novel approach for estimating lung tumor motion based on dynamic features in 4D-CT

Due to the high expenses involved, 4D-CT data for certain patients may only include five respiratory phases (0%, 20%, 40%, 60%, and 80%). This limitation can affect the subsequent planning of radiotherapy due to the absence of lung tumor information for the remaining five respiratory phases (10%, 30%, 50%, 70%, and 90%). This study aims to develop an interpolation method that can automatically derive tumor boundary contours for the five omitted phases using the available 5-phase 4D-CT data. The dynamic mode decomposition (DMD) method is a data-driven and model-free technique that can extract dynamic information from high-dimensional data. It enables the reconstruction of long-term dynamic patterns using only a limited number of time snapshots. The quasi-periodic motion of a deformable lung tumor caused by respiratory motion makes it suitable for treatment using DMD. The direct application of the DMD method to analyze the respiratory motion of the tumor is impractical because the tumor is three-dimensional and spans multiple CT slices. To predict the respiratory movement of lung tumors, a method called uniform angular interval (UAI) sampling was developed to generate snapshot vectors of equal length, which are suitable for DMD analysis. The effectiveness of this approach was confirmed by applying the UAI-DMD method to the 4D-CT data of ten patients with lung cancer. The results indicate that the UAI-DMD method effectively approximates the lung tumor’s deformable boundary surface and nonlinear motion trajectories. The estimated tumor centroid is within 2 mm of the manually delineated centroid, a smaller margin of error compared to the traditional BSpline interpolation method, which has a margin of 3 mm. This methodology has the potential to be extended to reconstruct the 20-phase respiratory movement of a lung tumor based on dynamic features from 10-phase 4D-CT data, thereby enabling more accurate estimation of the planned target volume (PTV).

CT respiratory motion synthesis using joint supervised and adversarial learning

Objective. Four-dimensional computed tomography (4DCT) imaging consists in reconstructing a CT acquisition into multiple phases to track internal organ and tumor motion. It is commonly used in radiotherapy treatment planning to establish planning target volumes. However, 4DCT increases protocol complexity, may not align with patient breathing during treatment, and lead to higher radiation delivery. Approach. In this study, we propose a deep synthesis method to generate pseudo respiratory CT phases from static images for motion-aware treatment planning. The model produces patient-specific deformation vector fields (DVFs) by conditioning synthesis on external patient surface-based estimation, mimicking respiratory monitoring devices. A key methodological contribution is to encourage DVF realism through supervised DVF training while using an adversarial term jointly not only on the warped image but also on the magnitude of the DVF itself. This way, we avoid excessive smoothness typically obtained through deep unsupervised learning, and encourage correlations with the respiratory amplitude. Main results. Performance is evaluated using real 4DCT acquisitions with smaller tumor volumes than previously reported. Results demonstrate for the first time that the generated pseudo-respiratory CT phases can capture organ and tumor motion with similar accuracy to repeated 4DCT scans of the same patient. Mean inter-scans tumor center-of-mass distances and Dice similarity coefficients were 1.97 mm and 0.63, respectively, for real 4DCT phases and 2.35 mm and 0.71 for synthetic phases, and compares favorably to a state-of-the-art technique (RMSim). Significance. This study presents a deep image synthesis method that addresses the limitations of conventional 4DCT by generating pseudo-respiratory CT phases from static images. Although further studies are needed to assess the dosimetric impact of the proposed method, this approach has the potential to reduce radiation exposure in radiotherapy treatment planning while maintaining accurate motion representation. Our training and testing code can be found at https://github.com/cyiheng/Dynagan.

Optimized raw data selection for artifact reduction of breathing controlled four-dimensional sequence scanning

Background and purposeEven with most breathing-controlled four-dimensional computed tomography (4DCT) algorithms image artifacts caused by single significant longer breathing still occur, resulting in negative consequences for radiotherapy. Our study presents first phantom examinations of a new optimized raw data selection and binning algorithm, aiming to improve image quality and geometric accuracy without additional dose exposure. Materials and methodsTo validate the new approach, phantom measurements were performed to assess geometric accuracy (volume fidelity, root mean square error, Dice coefficient of volume overlap) for one- and three-dimensional tumor motion trajectories with and without considering motion hysteresis effects. Scans without significantly longer breathing cycles served as references. ResultsMedian volume deviations between optimized approach and reference of at maximum 1% were obtained considering all movements. In comparison, standard reconstruction yielded median deviations of 9%, 21% and 12% for one-dimensional, three-dimensional, and hysteresis motion, respectively. Measurements in one- and three-dimensional directions reached a median Dice coefficient of 0.970 ± 0.013 and 0.975 ± 0.012, respectively, but only 0.918 ± 0.075 for hysteresis motions averaged over all measurements for the optimized selection. However, for the standard reconstruction median Dice coefficients were 0.845 ± 0.200, 0.868 ± 0.205 and 0.915 ± 0.075 for one- and three-dimensional as well as hysteresis motions, respectively. Median root mean square errors for the optimized algorithm were 30 ± 16HU2 and 120 ± 90HU2 for three-dimensional and hysteresis motions, compared to 212 ± 145HU2 and 130 ± 131HU2 for the standard reconstruction. ConclusionsThe algorithm was proven to reduce 4DCT-related artifacts due to missing projection data without further dose exposure. An improvement in radiotherapy treatment planning due to better image quality can be expected.

A model that predicts a real-time tumour surface using intra-treatment skin surface and end-of-expiration and end-of-inhalation planning CT images.

To develop a mapping model between skin surface motion and internal tumour motion and deformation using end-of-exhalation (EOE) and end-of-inhalation (EOI) 3D CT images for tracking lung tumours during respiration. Before treatment, skin and tumour surfaces were segmented and reconstructed from the EOE and the EOI 3D CT images. A non-rigid registration algorithm was used to register the EOE skin and tumour surfaces to the EOI, resulting in a displacement vector field that was then used to construct a mapping model. During treatment, the EOE skin surface was registered to the real-time, yielding a real-time skin surface displacement vector field. Using the mapping model generated, the input of a real-time skin surface can be used to calculate the real-time tumour surface. The proposed method was validated with and without simulated noise on 4D CT images from 15 patients at Léon Bérard Cancer Center and the 4D-lung dataset. The average centre position error, dice similarity coefficient (DSC), 95%-Hausdorff distance and mean distance to agreement of the tumour surfaces were 1.29 mm, 0.924, 2.76 mm, and 1.13 mm without simulated noise, respectively. With simulated noise, these values were 1.33 mm, 0.920, 2.79 mm, and 1.15 mm, respectively. A patient-specific model was proposed and validated that was constructed using only EOE and EOI 3D CT images and real-time skin surface images to predict internal tumour motion and deformation during respiratory motion. The proposed method achieves comparable accuracy to state-of-the-art methods with fewer pre-treatment planning CT images, which holds potential for application in precise image-guided radiation therapy.

Benchmarking machine learning-based real-time respiratory signal predictors in 4D SBRT.

Stereotactic body radiotherapy of thoracic and abdominal tumors has to account for respiratory intrafractional tumor motion. Commonly, an external breathing signal is continuously acquired that serves as a surrogate of the tumor motion and forms the basis of strategies like breathing-guided imaging and gated dose delivery. However, due to inherent system latencies, there exists a temporal lag between the acquired respiratory signal and the system response. Respiratory signal prediction models aim to compensate for the time delays and to improve imaging and dosedelivery. The present study explores and compares six state-of-the-art machine and deep learning-based prediction models, focusing on real-time and real-world applicability. All models and data are provided as open source and data to ensure reproducibility of the results and fosterreuse. The study was based on 2502 breathing signals ( h) acquired during clinical routine, split into independent training (50%), validation (20%), and test sets (30%). Input signal values were sampled from noisy signals, and the target signal values were selected from corresponding denoised signals. A standard linear prediction model (Linear), two state-of-the-art models in general univariate signal prediction (Dlinear, Xgboost), and three deep learning models (Lstm, Trans-Enc, Trans-TSF) were chosen. The prediction performance was evaluated for three different prediction horizons (480, 680, and 920ms). Moreover, the robustness of the different models when applied to atypical, that is, out-of-distribution (OOD) signals, was analyzed. The Lstm model achieved the lowest normalized root mean square error for all prediction horizons. The prediction errors only slightly increased for longer horizons. However, a substantial spread of the error values across the test signals was observed. Compared to typical, that is, in-distribution test signals, the prediction accuracy of all models decreased when applied to OOD signals. The more complex deep learning models Lstm and Trans-Enc showed the least performance loss, while the performance of simpler models like Linear dropped the most. Except for Trans-Enc, inference times for the different models allowed for real-time application. The application of the Lstm model achieved the lowest prediction errors. Simpler prediction filters suffer from limited signal history access, resulting in a drop in performance for OODsignals.

Dose-escalated SBRT for borderline and locally advanced pancreatic cancer. Feasibility, safety and preliminary clinical results of a multicenter study

BackgroundPancreatic Stereotactic Body Radiotherapy (SBRT) allows for the administration of a higher biologically effective doses (BED), that would be essential to achieve durable tumor control. Escalating treatment doses need a very accurate tumor positioning and motion control during radiotherapy.The aim of this study to assess the feasibility and safety of a Simultaneous Integrated Boost (SIB) dose-escalated protocol at 45 Gy, 50 Gy and 55 Gy in 5 consecutive daily fractions, in Border Line Resectable Pancreatic Cancer (BRCP) /Locally Advanced Pancreatic Cancer (LAPC) by means of a standard LINAC platform. MethodsPatients diagnosed of BRPC/LAPC, candidates for neoadjuvant chemotherapy and SBRT, in four university hospitals of the province of Las Palmas (Canary Islands, Spain) were included in this prospective study. Radiotherapy was administered using standard technology (LINACS) with advanced positioning (Lipiodol® and metallic stent used as fiducial markers) and tumor motion control (4D, DBH, Calypso®). There were 3 planned dose-escalated SIB groups, 45 Gy/5f (9 patients) 50 Gy/5f (9 + 9 patients) and 55 Gy/5f (9 patients). The defined primary end points of the study were the safety and feasibility of the proposed treatment protocol. Secondary endpoints included radiological tumor response after SBRT, local control and survival. ResultsFrom June 2017 to December 2022, sixty-two patients were initially assessed for eligibility in the study in the four participating centers, and 49 were candidates for chemotherapy (CHT). Forty-one were referred to radiotherapy after CHT and 33 finally were treated by escalated-dose SIB, 45 Gy (9 patients) 50 Gy (16 patients), 55 Gy(8 patients). All patients completed the scheduled treatment and no acute or late severe (≥grade3) gastrointestinal toxicity was observed.Local response was analyzed by CT/MRI two months after the end of SBRT. Ten patients (31,25 %) achieved objective response (2/9:45 Gy, 5/15:50 Gy, 3/8:55 Gy). Follow-up was closed as July 2023. Freedom from local progression at 1-2y were 89,3% (95 %CI:83,4–95,2%) and 66 % (95 %CI:54,6–77,4%) respectively. The 1-2y survival rates were 95,7% (95 %CI:91,4–100 % and 48,6% (95 %CI:37,7–59,5%) respectively. ConclusionThese promising results should be confirmed by further studies with larger sample size and extended follow-up period.

A 3D-printed phantom for stereotactic body radiation therapy simulation.

In modern radiation therapy for lung cancer, examining the uncertainty between tumor motion and beam delivery is vitally important. To lower the radiation dose delivery to the patient's normal tissue, narrowing the irradiation field margin to hit the tumor accurately is critical. Thus we proposed a phantom that simulates the thorax and lung tumor's motions by employing a 3D printing technique. The lung tumor is controlled by a linear miniature Delta robot arm, with a maximum displacement of 20 mm in each direction. When we simulated the thoracic breathing movements at 12 mm in A-P (Anterior-Posterior), the control errors were within 10%. The average tracking errors of the prosthetic tumor were within 1.1 mm. Therefore, the 3D-printed phantom with a robot arm can provide a reliable simulation for training and dosimetry measurement before lung radiotherapy, especially SBRT.

Respiratory motion modelling for MR-guided lung cancer radiotherapy: model development and geometric accuracy evaluation.

Objective.Respiratory motion of lung tumours and adjacent structures is challenging for radiotherapy. Online MR-imaging cannot currently provide real-time volumetric information of the moving patient anatomy, therefore limiting precise dose delivery, delivered dose reconstruction, and downstream adaptation methods.Approach.We tailor a respiratory motion modelling framework towards an MR-Linac workflow to estimate the time-resolved 4D motion from real-time data. We develop a multi-slice acquisition scheme which acquires thick, overlapping 2D motion-slices in different locations and orientations, interleaved with 2D surrogate-slices from a fixed location. The framework fits a motion model directly to the input data without the need for sorting or binning to account for inter- and intra-cycle variation of the breathing motion. The framework alternates between model fitting and motion-compensated super-resolution image reconstruction to recover a high-quality motion-free image and a motion model. The fitted model can then estimate the 4D motion from 2D surrogate-slices. The framework is applied to four simulated anthropomorphic datasets and evaluated against known ground truth anatomy and motion. Clinical applicability is demonstrated by applying our framework to eight datasets acquired on an MR-Linac from four lung cancer patients.Main results.The framework accurately reconstructs high-quality motion-compensated 3D images with 2 mm3isotropic voxels. For the simulated case with the largest target motion, the motion model achieved a mean deformation field error of 1.13 mm. For the patient cases residual error registrations estimate the model error to be 1.07 mm (1.64 mm), 0.91 mm (1.32 mm), and 0.88 mm (1.33 mm) in superior-inferior, anterior-posterior, and left-right directions respectively for the building (application) data.Significance.The motion modelling framework estimates the patient motion with high accuracy and accurately reconstructs the anatomy. The image acquisition scheme can be flexibly integrated into an MR-Linac workflow whilst maintaining the capability of online motion-management strategies based on cine imaging such as target tracking and/or gating.

Respiratory Motion Prediction with Empirical Mode Decomposition-Based Random Vector Functional Link

The precise prediction of tumor motion for radiotherapy has proven challenging due to the non-stationary nature of respiration-induced motion, frequently accompanied by unpredictable irregularities. Despite the availability of numerous prediction methods for respiratory motion prediction, the prediction errors they generate often suffer from large prediction horizons, intra-trace variabilities, and irregularities. To overcome these challenges, we have employed a hybrid method, which combines empirical mode decomposition (EMD) and random vector functional link (RVFL), referred to as EMD-RVFL. In the initial stage, EMD is used to decompose respiratory motion into interpretable intrinsic mode functions (IMFs) and residue. Subsequently, the RVFL network is trained for each obtained IMF and residue. Finally, the prediction results of all the IMFs and residue are summed up to obtain the final predicted output. We validated this proposed method on the benchmark datasets of 304 respiratory motion traces obtained from 31 patients for various prediction lengths, which are equivalent to the latencies of radiotherapy systems. In direct comparison with existing prediction techniques, our hybrid architecture consistently delivers a robust and highly accurate prediction performance. This proof-of-concept study indicates that the proposed approach is feasible and has the potential to improve the accuracy and effectiveness of radiotherapy treatment.

Beginning of clinical treatment using the 1.5 Tesla MR-Linac system in Japan: a narrative review.

In the field of radiation therapy, image-guided radiotherapy (IGRT) technology has been gradually improving and highly accurate radiation treatment has been possible. Research on IGRT using 1.5 Tesla magnetic resonance imaging (MRI) began in 1999, and a radiation therapy device called 1.5 Tesla magnetic resonance linear accelerator (MR-Linac), which combines a linear accelerator with 1.5 Tesla MRI, was developed in Europe. The aim of this review is to present an overview of 1.5 Tesla MR-Linac with a review of the literature and our experience. Reports related to 1.5 Tesla MR-Linac were searched for in PubMed and are discussed in relation to our experience. The 1.5 Tesla MR-Linac enables IGRT using 1.5 Tesla MRI, further enhancing the precision of radiation therapy. Position verification by cone-beam computed tomography (CBCT) is performed in many institutions, but soft tissue contrast is often unclear in CBCT images of the abdomen and mediastinal organs. Since the 1.5 Tesla MR-Linac allows position verification using MRI, position verification can be performed using clear MRI even in regions where CBCT is unclear. With the 1.5 Tesla MR-Linac, it is possible to perform online adaptive radiotherapy (ART) using 1.5 Tesla MRI. Online ART is a method in which images are acquired while the patient is on the treatment table. The method is based on the current condition of the organs in the body on that day and an optimal treatment field is recreated. Additionally, it allows monitoring of tumor motion using cine images obtained by 1.5 Tesla MRI during the delivery of X-ray radiation. A previous report showed that patients with prostate cancer who received radiotherapy by MR-Linac had fewer side effects than those in patients who received conventional CBCT radiation therapy. The 1.5 Tesla MR-Linac obtained CE-mark certification in Europe in August 2018 and it has been used for clinical treatment. In Japan, clinical treatment using this device started in 2021. By using 1.5 Tesla MR-Linac, patients can be provided with higher precision radiotherapy. In this review, we provide an overview of 1.5 Tesla MR-Linac.

Intra- and inter-fractional variations of tumors with fiducial markers measured using respiratory-correlated computed tomography images for respiratory gated lung stereotactic body radiation therapy.

This study evaluated the intra- and inter-fractional variation of tumors with fiducial markers (FMs), relative to the tumor-FM distance, to establish how close an FM should be inserted for respiratory-gated stereotactic body radiation therapy (RG-SBRT). Forty-five lung tumors treated with RG-SBRT were enrolled. End-expiratory computed tomography (CT) (CTplan) and four-dimensional-CT (4D-CT) scans were obtained for planning. End-expiratory CT (CTfr) scanning was performed before each fraction. The FMs were divided into two groups based on the median tumor-FM distance in the CTplan (Dp). For the intra-fractional variation, the correlations between the corresponding tumor and FM intra-fractional motions, defined as the centroid coordinates of those in each 0-90% phase, with the 50% phase of 4D-CT as the origin, were calculated in the left-right, anterior-posterior, and superior-inferior directions. Furthermore, the maximum difference in the tumor-FM distance in each phase of 4D-CT scan, based on those in the 50% phase of 4D-CT scan (Dmax), was obtained. Inter-fractional variation was defined as the maximum distance between the tumors in CTplan and CTfr, when the CT scans were fused based on each FM or vertebra. The median Dp was 26.1mm. While FM intra-fractional motions were significantly and strongly correlated with the tumor intra-fractional motions in only anterior-posterior and superior-inferior directions for the Dp>26mm group, they were significantly and strongly correlated in all directions for the Dp ≤ 26mm group. In all directions, Dmax values of the Dp ≤ 26mm group were lower than those of the Dp>26mm group. The inter-fractional variations based on the Dp ≤ 26mm were smaller than those on the Dp>26mm and on the vertebra in all directions. Regarding intra- and inter-fractional variation, FMs for Dp ≤ 26mm can increase the accuracy for RG-SBRT.

Sorting lung tumor volumes from 4D-MRI data using an automatic tumor-based signal reduces stitching artifacts.

To investigate whether a novel signal derived from tumor motion allows more precise sorting of 4D-magnetic resonance (4D-MR) image data than do signals based on normal anatomy, reducing levels of stitching artifacts within sorted lung tumor volumes. (4D-MRI) scans were collected for 10 lung cancer patients using a 2D T2-weighted single-shot turbo spin echo sequence, obtaining 25 repeat frames per image slice. For each slice, a tumor-motion signal was generated using the first principal component of movement in the tumor neighborhood (TumorPC1). Signals were also generated from displacements of the diaphragm (DIA) and upper and lower chest wall (UCW/LCW) and from slice body area changes (BA). Pearson r coefficients of correlations between observed tumor movement and respiratory signals were determined. TumorPC1, DIA, and UCW signals were used to compile image stacks showing each patient's tumor volume in a respiratory phase. Unsorted image stacks were also built for comparison. For each image stack, the presence of stitching artifacts was assessed by measuring the roughness of the compiled tumor surface according to a roughness metric (Rg). Statistical differences in weighted means of Rg between any two signals were determined using an exact permutation test. The TumorPC1 signal was most strongly correlated with superior-inferior tumor motion, and had significantly higher Pearson r values (median 0.86) than those determined for correlations of UCW, LCW, and BA with superior-inferior tumor motion (p<0.05). Weighted means of ratios of Rg values in TumorPC1 image stacks to those in unsorted, UCW, and DIA stacks were 0.67, 0.69, and 0.71, all significantly favoring TumorPC1 (p=0.02-0.05). For other pairs of signals, weighted mean ratios did not differ significantly from one. Tumor volumes were smoother in 3D image stacks compiled using the first principal component of tumor motion than in stacks compiled with signals based on normal anatomy.

A Comparison of Tumor Respiratory Motion Evaluation Methods Using Dynamic Thorax Motion Phantom

We evaluated the measurement accuracy and time efficiency of the tumor respiratory motion evaluation methods using a dynamic thorax motion phantom. A total of 12 patterns of 4DCT images with different tumor displacements and artifacts were used for the measurement. Three methods were employed to measure tumor motion. The first method was the manual delineation of the tumor on each phase CT image with a treatment planning system (RTPS [Manual]). The second method was the automatic delineation of the tumor structure by deformation and copying (RTPS [Auto]). The third method was tumor motion analysis software (Simple 4D Analysis Ver.1.3.1 [Simple 4D]; Triangle Products, Chiba, Japan). For each method, the difference between the phantom motion and the measured value was determined. The differences (mean±standard deviation: SD) in the superior-inferior direction for RTPS (Manual), RTPS (Auto), and Simple 4D in the without-artifact images were -0.6 mm±0.6 mm, -5.0 mm±2.2 mm, and -1.0 mm±0.0 mm, respectively. The difference in the left-right and anterior-posterior directions was within 1 mm for all methods. Furthermore, the time required for Simple 4D was shorter than for the other methods. Simple 4D showed the comparable measurement accuracy and improvement time efficiency to RTPS (Manual) and RTPS (Auto), and was useful for tumor respiratory motion analysis.

Assessing the Impact of a 1.5 T Transverse Magnetic Field in Radiotherapy for Esophageal Cancer Patients.

Purpose: Magnetic resonance (MR)-guided radiotherapy enables visualization of static anatomy, capturing tumor motion, and extracting quantitative image features for treatment verification and outcome monitoring. However, magnetic fields in online MR imaging (MRI) require efforts to ensure accurate dose measurements. This study aimed to assess the dosimetric impact of a 1.5 T magnetic field in esophageal cancer radiotherapy using MR-linac, exploring treatment adaptation potential and personalized medicine benefits. Methods: A prospective cohort study enrolled 100 esophageal squamous cell carcinoma patients undergoing 4DCT and 3DCT scans before radiotherapy. The heart was contoured on 3DCT, 4DCT end expiration (EE), and 4DCT end inhalation (EI) images by the same radiation oncologist. Reference RT plans were designed on 3DCT, with adjustments for different phases generating 5 plan types per patient. Variations in dose-volume parameters for organs at risk and the target area among different plans were compared using Monaco 5.40.04. Results: Slight dose distortions at air-tissue interfaces were observed in the magnetic field's presence. Dose at air-tissue interfaces (chest wall and heart wall) was slightly higher in some patients (3.0% tissue increased by 4.3 Gy on average) compared to nonmagnetic conditions. Average clinical target volume coverage V100 dropped from 99% to 95% compared to reference plans (planEI and planEE). Dose-volume histogram variation between the original plan and reference plans was within 2.3%. Superior-inferior (SI) direction displacement was significantly larger than lateral and anterior-posterior directions (P < .05). Conclusion: Significant SI direction shift in lower esophageal cancerous regions during RT indicates the magnetic field's dosimetric impact, including the electron return effect at tissue-air boundaries. Changes in OAR dose could serve as valuable indicators of organ impairment and target dose alterations, especially for cardiac tissue when using the 1.5 T linac method. Reoptimizing the plan with the magnetic field enhances the feasibility of achieving a clinically acceptable treatment plan for esophageal cancer patients.

Coarse-Super-Resolution-Fine Network (CoSF-Net): A Unified End-to-End Neural Network for 4D-MRI With Simultaneous Motion Estimation and Super-Resolution.

Four-dimensional magnetic resonance imaging (4D-MRI) is an emerging technique for tumor motion management in image-guided radiation therapy (IGRT). However, current 4D-MRI suffers from low spatial resolution and strong motion artifacts owing to the long acquisition time and patients' respiratory variations. If not managed properly, these limitations can adversely affect treatment planning and delivery in IGRT. In this study, we developed a novel deep learning framework called the coarse-super-resolution-fine network (CoSF-Net) to achieve simultaneous motion estimation and super-resolution within a unified model. We designed CoSF-Net by fully excavating the inherent properties of 4D-MRI, with consideration of limited and imperfectly matched training datasets. We conducted extensive experiments on multiple real patient datasets to assess the feasibility and robustness of the developed network. Compared with existing networks and three state-of-the-art conventional algorithms, CoSF-Net not only accurately estimated the deformable vector fields between the respiratory phases of 4D-MRI but also simultaneously improved the spatial resolution of 4D-MRI, enhancing anatomical features and producing 4D-MR images with high spatiotemporal resolution.

Pancreatic SABR using peritumoral fiducials, triggered imaging and breath-hold.

Background: We aim to present our linear accelerator-based workflow for pancreatic stereotactic ablative radiotherapy (SABR) in order to address the following issues: intrafractional organ motion management, Cone Beam CT (CBCT) image quality, residual errors with dosimetric consequences, treatment time, and clinical results. Methods: Between 2016 and 2021, 14 patients with locally advanced pancreatic cancer were treated with induction chemotherapy and SABR using volumetric modulated arc therapy (VMAT). Internal target volume (ITV) concept (5), phase-gated (4), or breath hold (5) techniques were used. Treatment was verified by CBCT before and after irradiation, while tumor motion was monitored and controlled by kV triggered imaging and beam hold using peritumoral surgical clips. Beam interruptions and treatment time were recorded. The CBCT image quality was scored and supplemented by an agreement analysis (Krippendorff's-α) of breath-hold CBCT images to determine the position of OARs relative to the planning risk volumes (PRV). Residual errors and their dosimetry impact were also calculated. Progression free (PFS) and overall survival (OS) were assessed by the Kaplan-Meier analysis with acute and late toxicity reporting (CTCAEv4). Results: On average, beams were interrupted once (range: 0-3) per treatment session on triggered imaging. The total median treatment time was 16.7 ± 10.8min, significantly less for breath-hold vs. phase-gated sessions (18.8 ± 6.2 vs. 26.5 ± 13.4, p < 0.001). The best image quality was achieved by breath hold CBCT. The Krippendorff's-α test showed a strong agreement among five radiation therapists (mean K-α value: 0.8 (97.5%). The mean residual errors were <0.2cm in each direction resulting in an average difference of <2% in dosimetry for OAR and target volume. Two patients received offline adaptation. The median OS/PFS after induction chemotherapy and SABR was 20/12months and 15/8months. No Gr. ≥2 acute/late RT-related toxicity was noted. Conclusion: Linear accelerator based pancreatic SABR with the combination of CBCT and triggered imaging + beam hold is feasible. Peritumoral fiducials improve utility while breath-hold CBCT provides the best image quality at a reasonable treatment time with offline adaptation possibilities. In well-selected cases, it can be an effective alternative in clinics where CBCT/MRI-guided online adaptive workflow is not available.

Characteristics of breathing-adapted gating using surface guidance for use in particle therapy: A phantom-based end-to-end test from CT simulation to dose delivery.

To account for intra-fractional tumor motion during dose delivery in radiotherapy, various treatment strategies are clinically implemented such as breathing-adapted gating and irradiating the tumor during specific breathing phases. In this work, we present a comprehensive phantom-based end-to-end test of breathing-adapted gating utilizing surface guidance for use in particle therapy. A commercial dynamic thorax phantom was used to reproduce regular and irregular breathing patterns recorded by the GateRT respiratory monitoring system. The amplitudes and periods of recorded breathing patterns were analysed and compared to planned patterns (ground-truth). In addition, the mean absolute deviations (MAD) and Pearson correlation coefficients (PCC) between the measurements and ground-truth were assessed. Measurements of gated and non-gated irradiations were also analysed with respect to dosimetry and geometry, and compared to treatment planning system (TPS). Further, the latency time of beam on/off was evaluated. Compared to the ground-truth, measurements performed with GateRT showed amplitude differences between 0.03±0.02mm and 0.26±0.03mm for regular and irregular breathing patterns, whilst periods of both breathing patterns ranged with a standard deviation between 10 and 190ms. Furthermore, the GateRT software precisely acquired breathing patterns with a maximum MAD of 0.30±0.23mm. The PCC constantly ranged between 0.998 and 1.000. Comparisons between TPS and measured dose profiles indicated absolute mean dose deviations within institutional tolerances of±5%. Geometrical beam characteristics also varied within our institutional tolerances of 1.5mm. The overall time delays were<60ms and thus within both recommended tolerances published by ESTRO and AAPM of 200 and 100ms, respectively. In this study, a non-invasive optical surface-guided workflow including image acquisition, treatment planning, patient positioning and gated irradiation at an ion-beam gantry was investigated, and shown to be clinically viable. Based on phantom measurements, our results show a clinically-appropriate spatial, temporal, and dosimetric accuracy when using surface guidance in the clinical setting, and the results comply with international and institutional guidelines and tolerances.

4D-MRI assisted stereotactic body radiation therapy for unresectable colorectal cancer liver metastases

This study evaluated the feasibilities and outcomes following four-dimensional magnetic resonance imaging (4D-MRI) assisted stereotactic body radiation therapy (SBRT) for unresectable colorectal liver metastases (CRLMs). From March 2018 to January 2022, we identified 76 unresectable CRLMs patients with 123 lesions who received 4D-MRI guided SBRT in our institution. 4D-MRI simulation with or without abdominal compression was conducted for all patients. The prescription dose was 50–65 Gy in 5–12 fractions. The image quality of computed tomography (CT) and MRI were compared using the Clarity Score. Clinical outcomes and toxicity profiles were evaluated. 4D-MRI improved the image quality compared with CT images (mean Clarity Score: 1.67 vs 2.88, P < 0.001). The abdominal compression reduced motions in cranial–caudal direction (P = 0.03) with two phase T2 weighted images assessing tumor motion. The median follow-up time was 12.5 months. For 98 lesions assessed for best response, the complete response, partial response and stable disease rate were 57.1 %, 30.6 % and 12.2 %, respectively. The local control (LC) rate at 1 year was 97.3 %. 46.1 % of patients experienced grade 1–2 toxicities and only 2.6 % patients experienced grade 3 hematologic toxicities. The 4D-MRI technique allowed accurate target delineation and motion tracking in unresectable CRLMs patients. Favorable LC rate and mild toxicities were achieved. This study provided evidence for using 4D-MRI assisted SBRT as an alternative treatment in unresectable CRLMs.